Vibe Coding Explained: From AI Prototype to Production
Key Takeaways
- A convincing demo does not establish production readiness.
- Review generated code and test important failure paths.
- Keep useful existing work when evidence supports it; a rewrite is not automatic.
Vibe coding commonly describes building software through natural-language instructions to an AI tool, with limited direct attention to the generated code. It can be useful for exploring an interface or testing an idea. Once real customers, business data or important transactions are involved, the result needs deliberate engineering review.
What can a prototype prove?
A prototype can show whether people understand a workflow, whether a proposed interface is useful and what requirements were missing from the initial idea. It cannot, by itself, establish secure access controls, reliable payments or recoverable operations.
Consider an illustrative booking app that works when one person tests it. Two users requesting the last appointment at the same time may expose a concurrency bug. The problem is not how quickly the interface was generated; it is whether the underlying booking rule was implemented and tested.
Review these areas before launch
| Area | Evidence to request |
|---|---|
| Access control | Tests showing users cannot read or change another user’s records |
| Data integrity | Correct behaviour for duplicate requests and simultaneous changes |
| Payments | Validated events, retry handling and consistent access decisions |
| Secrets and dependencies | Controlled configuration and an update process |
| Operations | Logs, alerts, backups and a tested restore procedure |
A practical review sequence
- Inventory the code, services, accounts and data.
- Trace one complete customer journey through the application.
- Review the highest-impact permissions and business rules.
- Test failure paths on a staging environment.
- Document reproducible defects and prioritise them.
- Fix, retest and release with a rollback plan.
Internal tools deserve appropriate security too. They may hold sensitive operational information even when the audience is small. Restrict access and avoid putting real customer data into an unreviewed demo.
Do you need to rewrite everything?
Not automatically. A useful review distinguishes working, maintainable components from fragile or unsafe ones. Retain code that meets requirements, refactor where it reduces risk and replace components when there is a concrete reason. “AI wrote it” is not enough evidence for a full rewrite.
For an early product, our SaaS MVP validation guide helps decide what the next investment should test. For launch planning, use our SaaS delivery roadmap.
How should a business work with AI coding tools?
Keep changes reviewable, use version control and define acceptance criteria before requesting a feature. Ask the tool to explain assumptions and generate meaningful tests for important behaviour. A passing test that merely repeats the implementation is weak evidence; test the actual business rule.
What should an engineering handover include?
Provide the repository, deployment process, environment-variable names, service ownership, known issues and a description of the core user journey. Share credentials through an appropriate secure channel, not in a project brief or public document.
Ask Ananas IT to review your prototype. We can help identify the smallest set of changes needed for a dependable first release.
